SEME ( Sampling with Expectation maximization for Motif Elicitation) is a de novo motif discovery algorithm which uses pure probabilistic mixture model to model the motif’s binding features and uses expectation maximization (EM) algorithms to simultaneously learn the sequence motif, position, and sequence rank preferences without asking for any prior knowledge from the user.
- Linux/ Windows/ MacOsX
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J Comput Biol. 2013 Mar;20(3):237-48. doi: 10.1089/cmb.2012.0233.
Simultaneously learning DNA motif along with its position and sequence rank preferences through expectation maximization algorithm.
Zhang Z, Chang CW, Hugo W, Cheung E, Sung WK.